Research on opposition-based enhanced fireworks algorithm optimization for mine ventilation network

A non-linear unrestraint optimization model of mine ventilation network was established which took the minimum total energy consumption of mine ventilation network as optimization objective. In order to improve optimization ability and convergence speed of the model, an opposition-based enhanced fireworks algorithm(OBEFWA) was proposed. Firstly, population initialization strategy based on opposition-based learning and uniform randomization is adopted, and uniform randomization population generated by the strategy is competed with opposition-based population, so that the optimal initial population is selected as starting point of subsequent search. Secondly, fireworks explosion radius is finely controlled, so that explosion radius of fireworks populations of different generations shows non-linear decline, and that of the same population generation is coordinated and distriblted according to their own fitness values. The minimum dynamic threshold is set to decrease waste of search resources. Finally, selection strategy of elite opposition-based learning is adopted to strengthen search for neighborhood of elite fireworks, so as to improve global exploration ability of the algorithm. The experimental results show that total energy consumption of mine ventilation network optimized by OBEFWA decreases about 23.2% which meets adjustment constraints and wind demand of actual ventilation network, and OBEFWA has better optimization effect than particle swarm optimization algorithm and enhanced fireworks algorithm.